Search AMIA.org

Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.5-turbo, Flan-T5-xl, Flan-T5-xxl [...]

To evaluate the use of patient portal messaging to recruit individuals historically underrepresented in biomedical research (UBR) to the All of Us Research Program (AoURP) at a single recruitment site.

The aim of this study was to investigate GPT-3.5 in generating and coding medical documents with International Classification of Diseases (ICD)-10 codes for data augmentation on low-resource labels.

Clinical research networks facilitate collaborative research, but data sharing remains a common barrier.

To address the challenges of sharing clinical research data through the implementation of cloud-based virtual desktops, enhancing collaboration among researchers while maintaining data security.

Accelerating demand for patient messaging has impacted the practice of many providers. Messages are not recommended for urgent medical issues, but some do require rapid attention. This presents an opportunity for artificial intelligence (AI) methods to prioritize review of messages. Our study aimed to highlight some patient portal messages for prioritized review using a custom AI system integrated into the electronic health record (EHR).

We evaluated the effectiveness and implementability of a standardized EHR-integrated handoff report to support intraoperative handoffs.

Public sharing of de-identified biomedical data promotes collaboration between researchers and accelerates the development of disease prevention and treatment strategies. However, open-access data sharing presents challenges to researchers who need to protect the privacy of study participants, ensure that data are used appropriately, and acknowledge the inputs of all involved researchers. This article presents an approach to data sharing which addresses the above challenges by using a publicly available dashboard [...]

Large Language Models (LLMs) have been proposed as a solution to address high volumes of Patient Medical Advice Requests (PMARs). This study addresses whether LLMs can generate high quality draft responses to PMARs that satisfies both patients and clinicians with prompt engineering.

The Department of Veterans Affairs (VA) is transitioning from its legacy electronic health record (EHR) to a new commercial EHR in a nationwide, rolling-wave transition. We evaluated clinician and staff experiences to identify strategies to improve future EHR rollouts.